SK-TDA4VM: YOLOPv2 Lane Line Detection distortion when off-loading to C7x

Charanjit Singh

Part Number: SK-TDA4VM
Other Parts Discussed in Thread: TDA4VM

Tool/software:

Hi,

As per details provided in the thread https://e2e.ti.com/support/processors-group/processors/f/processors-forum/1493749/sk-tda4vm-yolopv2-lane-line-detection-distortion-when-off-loading-to-c7x/5802503#5802503 I am getting a fixed pattern output from the YOLOPv2 model compiled for the SK-TDA4VM.

Without offload

With offload

Without offload

With offload

As suggested by Christina Kuruvilla, I tried using dynamic input dimensions when converting the model to ONNX from Pytorch, but the dynamic input model does not compile with any offloading for TIDL


"text">root@03d5f96f5a40:/home/root/yolopv2# python3 sample.py -c --source calib-imgs s_folder': 'custom-artifacts/yolopv2', 'tidl_tools_path': '/home/root/tools/AM68PA/tidl_tools', 'advanced_options:c7x_firmware_version': '10_01_04_00'} [Model Compilation Started] ========================= perform the following stages: [Version Summary] ============================== ------------------------------------------------ |              10_01_04_00             | ------------------------------------------------ |              10_01_04_00             | ------------------------------------------------ |                1.15.0                | ------------------------------------------------ |                  17                  | ------------------------------------------------ [Parsing Started] ============================== WARNING: Network not identified as Object Detection network : (1) Ignore if network is not Object Detection network (2) If network is Object Detection network, please specify "model_type":"OD" as part of OSRT compilation options Subgraph Information Summary ------------------------- ------------------------------------------------ No. of Nodes       |   Number of Subgraphs   | ------------------------------------------------ 0 |                       0 | 431 |                       x | ------------------------------------------------ -------------------------------------------------------------------------------------------------------- Node Name       |                                         Reason                                         | -------------------------------------------------------------------------------------------------------- | Layer 0 - op type Conv, Unknown input dimension, not supported by TIDL                 | | Layer 1 - op type Sigmoid, Unknown input dimension, not supported by TIDL              | | Layer 2 - op type Mul, Unknown input dimension, not supported by TIDL                  | | Layer 3 - op type Conv, Unknown input dimension, not supported by TIDL                 | | Layer 4 - op type Sigmoid, Unknown input dimension, not supported by TIDL              | | Layer 5 - op type Mul, Unknown input dimension, not supported by TIDL                  | | Layer 6 - op type Conv, Unknown input dimension, not supported by TIDL                 | | Layer 7 - op type Sigmoid, Unknown input dimension, not supported by TIDL              | | Layer 8 - op type Mul, Unknown input dimension, not supported by TIDL                  | | Layer 9 - op type Conv, Unknown input dimension, not supported by TIDL                 | | Layer 10 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 11 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 12 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 13 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 14 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 15 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 16 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 17 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 18 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 19 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 20 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 21 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 22 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 23 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 24 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 25 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 26 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 27 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 28 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 29 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 30 - op type Concat, Unknown input dimension, not supported by TIDL              | | Layer 31 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 32 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 33 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 34 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 35 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 36 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 37 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 38 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 39 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 40 - op type MaxPool, Unknown input dimension, not supported by TIDL             | | Layer 41 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 42 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 43 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 44 - op type Concat, Unknown input dimension, not supported by TIDL              | | Layer 45 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 46 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 47 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 48 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 49 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 50 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 51 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 52 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 53 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 54 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 55 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 56 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 57 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 58 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 59 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 60 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 61 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 62 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 63 - op type Concat, Unknown input dimension, not supported by TIDL              | | Layer 64 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 65 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 66 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 67 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 68 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 69 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 70 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 71 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 72 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 73 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 74 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 75 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 76 - op type MaxPool, Unknown input dimension, not supported by TIDL             | | Layer 77 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 78 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 79 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 80 - op type Concat, Unknown input dimension, not supported by TIDL              | | Layer 81 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 82 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 83 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 84 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 85 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 86 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 87 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 88 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 89 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 90 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 91 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 92 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 93 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 94 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 95 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 96 - op type Conv, Unknown input dimension, not supported by TIDL                | | Layer 97 - op type Sigmoid, Unknown input dimension, not supported by TIDL             | | Layer 98 - op type Mul, Unknown input dimension, not supported by TIDL                 | | Layer 99 - op type Concat, Unknown input dimension, not supported by TIDL              | | Layer 100 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 101 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 102 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 103 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 104 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 105 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 106 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 107 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 108 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 109 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 110 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 111 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 112 - op type MaxPool, Unknown input dimension, not supported by TIDL            | | Layer 113 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 114 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 115 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 116 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 117 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 118 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 119 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 120 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 121 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 122 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 123 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 124 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 125 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 126 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 127 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 128 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 129 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 130 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 131 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 132 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 133 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 134 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 135 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 136 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 137 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 138 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 139 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 140 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 141 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 142 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 143 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 144 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 145 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 146 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 147 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 148 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 149 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 150 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 151 - op type MaxPool, Unknown input dimension, not supported by TIDL            | | Layer 152 - op type MaxPool, Unknown input dimension, not supported by TIDL            | | Layer 153 - op type MaxPool, Unknown input dimension, not supported by TIDL            | | Layer 154 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 155 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 156 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 157 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 158 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 159 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 160 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 161 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 162 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 163 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 164 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 165 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 166 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 167 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 168 - op type Resize, Unknown input dimension, not supported by TIDL             | | Layer 169 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 170 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 171 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 172 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 173 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 174 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 175 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 176 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 177 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 178 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 179 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 180 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 181 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 182 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 183 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 184 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 185 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 186 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 187 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 188 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 189 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 190 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 191 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 192 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 193 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 194 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 195 - op type Resize, Unknown input dimension, not supported by TIDL             | | Layer 196 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 197 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 198 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 199 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 200 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 201 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 202 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 203 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 204 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 205 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 206 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 207 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 208 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 209 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 210 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 211 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 212 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 213 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 214 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 215 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 216 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 217 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 218 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 219 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 220 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 221 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 222 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 223 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 224 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 225 - op type MaxPool, Unknown input dimension, not supported by TIDL            | | Layer 226 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 227 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 228 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 229 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 230 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 231 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 232 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 233 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 234 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 235 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 236 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 237 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 238 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 239 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 240 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 241 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 242 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 243 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 244 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 245 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 246 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 247 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 248 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 249 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 250 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 251 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 252 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 253 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 254 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 255 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 256 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 257 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 258 - op type MaxPool, Unknown input dimension, not supported by TIDL            | | Layer 259 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 260 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 261 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 262 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 263 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 264 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 265 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 266 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 267 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 268 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 269 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 270 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 271 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 272 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 273 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 274 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 275 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 276 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 277 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 278 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 279 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 280 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 281 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 282 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 283 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 284 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 285 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 286 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 287 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 288 - op type Add, Unknown input dimension, not supported by TIDL                | | Layer 289 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 290 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 291 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 292 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 293 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 294 - op type Add, Unknown input dimension, not supported by TIDL                | | Layer 295 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 296 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 297 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 298 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 299 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 300 - op type Add, Unknown input dimension, not supported by TIDL                | | Layer 301 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 302 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 303 - op type SequenceConstruct, Unknown input dimension, not supported by TIDL  | | Layer 304 - op type Shape, Unknown input dimension, not supported by TIDL              | | Layer 305 - op type Gather, Unknown input dimension, not supported by TIDL             | | Layer 306 - op type Unsqueeze, Unknown input dimension, not supported by TIDL          | | Layer 307 - op type Gather, Unknown input dimension, not supported by TIDL             | | Layer 308 - op type Unsqueeze, Unknown input dimension, not supported by TIDL          | | Unsupported data type                                                                  | | Unsupported data type                                                                  | | Layer 311 - op type GlobalAveragePool, Unknown input dimension, not supported by TIDL  | | Layer 312 - op type Reshape, Unknown input dimension, not supported by TIDL            | | Layer 313 - op type MatMul, Unknown input dimension, not supported by TIDL             | | Layer 314 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 315 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 316 - op type MatMul, Unknown input dimension, not supported by TIDL             | | Layer 317 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 318 - op type Reshape, Unknown input dimension, not supported by TIDL            | | Layer 319 - op type Expand, Unknown input dimension, not supported by TIDL             | | Layer 320 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 321 - op type Add, Unknown input dimension, not supported by TIDL                | | Layer 322 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 323 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 324 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 325 - op type ConvTranspose, Unknown input dimension, not supported by TIDL      | /BatchNormalization_2 | Layer 326 - op type BatchNormalization, Unknown input dimension, not supported by TIDL | | Layer 327 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 328 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 329 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 330 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 331 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 332 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 333 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 334 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 335 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 336 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 337 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 338 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 339 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 340 - op type Concat, Unknown input dimension, not supported by TIDL             | /BatchNormalization_3 | Layer 341 - op type BatchNormalization, Unknown input dimension, not supported by TIDL | | Layer 342 - op type LeakyRelu, Unknown input dimension, not supported by TIDL          | | Layer 343 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 344 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 345 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 346 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 347 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 348 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 349 - op type ConvTranspose, Unknown input dimension, not supported by TIDL      | /BatchNormalization_4 | Layer 350 - op type BatchNormalization, Unknown input dimension, not supported by TIDL | | Layer 351 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 352 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 353 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 354 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 355 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 356 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 357 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 358 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 359 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 360 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 361 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 362 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 363 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 364 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 365 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 366 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 367 - op type Concat, Unknown input dimension, not supported by TIDL             | /BatchNormalization_5 | Layer 368 - op type BatchNormalization, Unknown input dimension, not supported by TIDL | | Layer 369 - op type LeakyRelu, Unknown input dimension, not supported by TIDL          | | Layer 370 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 371 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 372 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 373 - op type ConvTranspose, Unknown input dimension, not supported by TIDL      | /BatchNormalization_6 | Layer 374 - op type BatchNormalization, Unknown input dimension, not supported by TIDL | | Layer 375 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 376 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 377 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 378 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 379 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 380 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 381 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 382 - op type Resize, Unknown input dimension, not supported by TIDL             | | Layer 383 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 384 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 385 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 386 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 387 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 388 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 389 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 390 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 391 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 392 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 393 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 394 - op type Concat, Unknown input dimension, not supported by TIDL             | | Layer 395 - op type BatchNormalization, Unknown input dimension, not supported by TIDL | | Layer 396 - op type LeakyRelu, Unknown input dimension, not supported by TIDL          | | Layer 397 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 398 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 399 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 400 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 401 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 402 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 403 - op type Resize, Unknown input dimension, not supported by TIDL             | | Layer 404 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 405 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 406 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 407 - op type Resize, Unknown input dimension, not supported by TIDL             | | Layer 408 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 409 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 410 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 411 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 412 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 413 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 414 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 415 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 416 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 417 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 418 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 419 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 420 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 421 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 422 - op type Concat, Unknown input dimension, not supported by TIDL             | /BatchNormalization_1 | Layer 423 - op type BatchNormalization, Unknown input dimension, not supported by TIDL | | Layer 424 - op type LeakyRelu, Unknown input dimension, not supported by TIDL          | | Layer 425 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 426 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | | Layer 427 - op type Mul, Unknown input dimension, not supported by TIDL                | | Layer 428 - op type Resize, Unknown input dimension, not supported by TIDL             | | Layer 429 - op type Conv, Unknown input dimension, not supported by TIDL               | | Layer 430 - op type Sigmoid, Unknown input dimension, not supported by TIDL            | -------------------------------------------------------------------------------------------------------- [Parsing Completed] ============================= [W:onnxruntime:, graph.cc:3533 CleanUnusedInitializersAndNodeArgs] Removing initializer '781'. It is not used by any node and should be removed from the model. [W:onnxruntime:, graph.cc:3533 CleanUnusedInitializersAndNodeArgs] Removing initializer '780'. It is not used by any node and should be removed from the model. [W:onnxruntime:, graph.cc:3533 CleanUnusedInitializersAndNodeArgs] Removing initializer '779'. It is not used by any node and should be removed from the model. 'CPUExecutionProvider']
It has been more than a month since my original question, so I would appreciate a timely reply.

Relevant code used for compiling is as follows:
sample.py
import os
import copy
import time
import argparse

import cv2
import numpy as np

import onnxruntime

from utils import utils_onnx
from pathlib import Path

from utils.utils_onnx import increment_path, LoadImages
from da_seg_mid import calc_seg_mid, draw_seg_mid, calc_ll_mid, draw_ll_mid, get_best_lane
from steering import proc_algorithm, send_steering_command, map_to_joystick_range, connect_client

import shutil
import platform

def get_args():
    parser = argparse.ArgumentParser()

    parser.add_argument(
        '--video',
        type=str,
        default='sample.mp4',
    )
    parser.add_argument(
        "-c", "--compile",
        action="store_true",
        help="Run in Model compilation mode"
    )
    parser.add_argument(
        "-d", "--disable_offload",
        action="store_true",
        help="Disable offload to TIDL"
    )
    parser.add_argument(
        '--model',
        type=str,
        default='weight/YOLOPv2.onnx',
    )
    parser.add_argument(
        '--score_th',
        type=float,
        default=0.3,
    )
    parser.add_argument(
        '--nms_th',
        type=float,
        default=0.45,
    )
    parser.add_argument(
            '--save',
            action='store_true',
            help='save images/videos'
    )
    parser.add_argument(
            '--source',
            type=str,
            default='sample.mp4',
            help='input source'
    )
    parser.add_argument(
            '--project',
            default='runs/detect',
            help='save results to project/name'
    )
    parser.add_argument(
            '--name',
            default='exp',
            help='save results to project/name'
    )
    parser.add_argument(
            '--exist-ok',
            action='store_true',
            help='existing project/name ok, do not increment'
    )
    parser.add_argument(
            '--screen',
            type=int,
            default=1,
            help='Screen number you want to use for capturing'
    )
    parser.add_argument(
            '--log',
            type=int,
            default=2,
            help='Log severity level.0:Verbose, 1:Info, 2:Warning. 3:Error, 4:Fatal.'
    )
    args = parser.parse_args()

    return args


def run_inference(
    onnx_session,
    image,
    score_th,
    nms_th,
):
    # 前処理
    # パディング処理を実行
    input_image = copy.deepcopy(image)
    input_image, _, (pad_w, pad_h) = utils_onnx.letterbox(input_image)

    # BGR→RGB変換
    input_image = input_image[:, :, ::-1].transpose(2, 0, 1)

    # PyTorch Tensorに変換
    input_image = np.ascontiguousarray(input_image)

    # 正規化
    input_image = input_image.astype('float32')
    input_image /= 255.0

    # NCHWに変換
    input_image = np.expand_dims(input_image, axis=0)

    # 推論
    input_details = onnx_session.get_inputs()
    input_name = input_details[0].name
    input_shape = input_details[0].shape
    results = onnx_session.run(None, {input_name: input_image})

    result_dets = []
    result_dets.append(results[0][0])
    result_dets.append(results[0][1])
    result_dets.append(results[0][2])

    anchor_grid = []
    anchor_grid.append(results[1])
    anchor_grid.append(results[2])
    anchor_grid.append(results[3])

    # 後処理
    # 車検出
    result_dets = utils_onnx.split_for_trace_model(
        result_dets,
        anchor_grid,
    )

    result_dets = utils_onnx.non_max_suppression(
        result_dets,
        conf_thres=score_th,
        iou_thres=nms_th,
    )

    bboxes = []
    scores = []
    class_ids = []
    for result_det in result_dets:
        if len(result_det) > 0:
            # バウンディングボックスのスケールを調整
            result_det[:, :4] = utils_onnx.scale_coords(
                input_image.shape[2:],
                result_det[:, :4],
                image.shape,
            ).round()

            # バウンディングボックス、スコア、クラスIDを取得
            for *xyxy, score, class_id in reversed(result_det):
                x1, y1 = xyxy[0], xyxy[1]
                x2, y2 = xyxy[2], xyxy[3]

                bboxes.append([int(x1), int(y1), int(x2), int(y2)])
                scores.append(float(score))
                class_ids.append(int(class_id))

    # 路面セグメンテーション
    result_road_seg = utils_onnx.driving_area_mask(
        results[4],
        (pad_w, pad_h),
    )

    # レーンセグメンテーション
    result_lane_seg = utils_onnx.lane_line_mask(
        results[5],
        (pad_w, pad_h),
    )

    return (bboxes, scores, class_ids), result_road_seg, result_lane_seg


def main():
    # 引数
    args = get_args()
    # Enforce compilation on x86 only

    if platform.machine() == "aarch64" and args.compile == True:
        print(
            "Compilation of models is only supported on x86 machine \n\
            Please do the compilation on PC and copy artifacts for running on TIDL devices "
        )
        exit(-1)

    source = args.source
    screen = args.screen
    save_img = args.save # save inference images

    if save_img:
        save_dir = Path(increment_path(Path(args.project) / args.name, exist_ok=args.exist_ok))  # increment run
        save_dir.mkdir(parents=True, exist_ok=True)  # make save_dir

    video_path = args.video

    model_path = args.model
    score_th = args.score_th
    nms_th = args.nms_th

    # ONNXファイル有無確認
    if not os.path.isfile(model_path):
        import urllib.request
        url = 'https://github.com/Kazuhito00/YOLOPv2-ONNX-Sample/releases/download/v0.0.0/YOLOPv2.onnx'
        weights_save_path = 'weight/YOLOPv2.onnx'

        print('Start Download:YOLOPv2.onnx')
        urllib.request.urlretrieve(url, weights_save_path)
        print('Finish Download')

    # モデルロード
    c7x_firmware_version = "10_01_04_00" #set variable for firmware version.
    compile_options = {}
    so = onnxruntime.SessionOptions()
    so.log_severity_level = args.log
    compile_options['artifacts_folder'] = 'custom-artifacts/yolopv2'
    compile_options['tidl_tools_path'] = os.environ.get("TIDL_TOOLS_PATH")
    compile_options['advanced_options:c7x_firmware_version'] = c7x_firmware_version
    print(f"compile_options: {compile_options}")

    calib_images = os.listdir('calib-imgs')

    if args.compile:
        import onnx
        os.makedirs(compile_options["artifacts_folder"], exist_ok=True)
        for root, dirs, files in os.walk(
            compile_options["artifacts_folder"], topdown=False
        ):
            [os.remove(os.path.join(root, f)) for f in files]
            [os.rmdir(os.path.join(root, d)) for d in dirs]

        EP_list = ['TIDLCompilationProvider', 'CPUExecutionProvider']
        # Shape inference needed for offload to C7x
        onnx.shape_inference.infer_shapes_path(
            model_path, model_path
        )

        onnx_session = onnxruntime.InferenceSession(
            model_path,
            providers=EP_list,
            provider_options=[compile_options, {}],
            sess_options=so
        )
        print(f"EP: {onnx_session.get_providers()}")

    elif args.disable_offload:
        EP_list = ['CPUExecutionProvider']

        onnx_session = onnxruntime.InferenceSession(
            model_path,
            providers=EP_list,
            sess_options=so
        )
        print(f"EP: {onnx_session.get_providers()}")
    else:
        EP_list = ['TIDLExecutionProvider', 'CPUExecutionProvider']

        onnx_session = onnxruntime.InferenceSession(
            model_path,
            providers=EP_list,
            provider_options=[compile_options, {}],
            sess_options=so
        )
        print(f"EP: {onnx_session.get_providers()}")

    vid_path, vid_writer = None, None
    dataset = LoadImages(source, screen=screen)


    # ビデオ読み込み
    #video_capture = cv2.VideoCapture(video_path)

    for path, frame, vid_cap in dataset:
        start_time = time.time()

        # 画像読み込み
#        ret, frame = video_capture.read()
#        if not ret:
#            break

        # 推論
        (bboxes, scores, class_ids), road_seg, lane_seg = run_inference(
            onnx_session,
            frame,
            score_th,
            nms_th,
        )

        fps = 1 / (time.time() - start_time)

        # 推論結果可視化
        debug_image = draw_debug_image(
            frame,
            (bboxes, scores, class_ids),
            road_seg,
            lane_seg,
            fps,
        )


        out_pipeline = "appsrc ! videoconvert ! kmssink driver-name=tidss sync=false"

        # Save image/video with segments, lanes and mid points shown. Not relevant for inference though.
        if save_img:
            p = Path(path)
            save_path = str(save_dir / p.name)  # img.jpg
            if dataset.mode == 'image':
                cv2.imwrite(save_path, debug_image)
                print(f" The image with the result is saved in: {save_path}")
            else:  # 'video' or 'stream'
                if vid_path != save_path:  # new video
                    vid_path = save_path
                    if isinstance(vid_writer, cv2.VideoWriter):
                        vid_writer.release()  # release previous video writer
                    if vid_cap:  # video
                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
                        #w = int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH))
                        #h = int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        w,h = debug_image.shape[1], debug_image.shape[0]
                    else:  # stream
                        fps, w, h = 30, debug_image.shape[1], debug_image.shape[0]
                        save_path += '.mp4'
                    vid_writer = cv2.VideoWriter(out_pipeline, cv2.CAP_GSTREAMER, 0, fps, (w, h))
                vid_writer.write(debug_image)

#    video_capture.release()
#    cv2.destroyAllWindows()


def draw_debug_image(
    image,
    car_dets,
    road_seg,
    lane_seg,
    fps,
):
    debug_image = copy.deepcopy(image)

    # 路面セグメンテーション
    image_width, image_height = debug_image.shape[1], debug_image.shape[0]

    # マスク画像を生成
    road_mask = np.stack((road_seg, ) * 3, axis=-1).astype('float32')
    road_mask = cv2.resize(
        road_mask,
        dsize=(image_width, image_height),
        interpolation=cv2.INTER_LINEAR,
    )
    road_mask = np.where(road_mask > 0.5, 0, 1)

    # マスク画像と画像を合成
    bg_image = np.zeros(debug_image.shape, dtype=np.uint8)
    bg_image[:] = [0, 255, 0]
    road_mask_image = np.where(road_mask, debug_image, bg_image)

    # 半透明画像として合成
    debug_image = cv2.addWeighted(debug_image, 0.5, road_mask_image, 0.5, 1.0)

    # レーンセグメンテーション
    # マスク画像を生成
    road_mask = np.stack((lane_seg, ) * 3, axis=-1).astype('float32')
    road_mask = cv2.resize(
        road_mask,
        dsize=(image_width, image_height),
        interpolation=cv2.INTER_LINEAR,
    )
    road_mask = np.where(road_mask > 0.5, 0, 1)

    # マスク画像と画像を合成
    bg_image = np.zeros(debug_image.shape, dtype=np.uint8)
    bg_image[:] = [0, 0, 255]
    road_mask_image = np.where(road_mask, debug_image, bg_image)

    # 半透明画像として合成
    debug_image = cv2.addWeighted(debug_image, 0.5, road_mask_image, 0.5, 1.0)

    # 車検出結果
    for bbox, score, class_id in zip(*car_dets):
        # バウンディングボックス
        cv2.rectangle(
            debug_image,
            pt1=(bbox[0], bbox[1]),
            pt2=(bbox[2], bbox[3]),
            color=(0, 255, 255),
            thickness=2,
        )

        # クラスID、スコア
#        text = '%s:%s' % (str(class_id), '%.2f' % score)
#        cv2.putText(
#            debug_image,
#            text,
#            (bbox[0], bbox[1] - 10),
#            cv2.FONT_HERSHEY_SIMPLEX,
#            0.7,
#            color=(0, 255, 255),
#            thickness=2,
#        )

    # 処理時間
    cv2.putText(
        debug_image,
        "FPS:" + '{:.1f}'.format(fps),
        (10, 30),
        cv2.FONT_HERSHEY_SIMPLEX,
        0.8,
        (255, 0, 0),
        2,
        cv2.LINE_AA,
    )

    return debug_image


if __name__ == "__main__":
    main()

utils_onnx.py
import copy
import time

import cv2
import numpy as np
from pathlib import Path
import glob
import re
import os
#from screen_grab import grab

class LoadImages:  # for inference
    def __init__(self, path, screen=1):
        self.dev = False
        self.grab_screen = False
        p = str(Path(path).absolute())  # os-agnostic absolute path
        print(p)
        if '*' in p:
            files = sorted(glob.glob(p, recursive=True))  # glob
        elif os.path.isdir(p):
            files = sorted(glob.glob(os.path.join(p, '*.*')))  # dir
        elif os.path.isfile(p):
            files = [p]  # files
        elif p.startswith("/dev/video"):
            files = [p]
            self.dev = True
#        elif "screengrab" in p:
#            files = [p]
#            self.grab_screen = True
        else:
            raise Exception(f'ERROR: {p} does not exist')

        img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo']  # acceptable image suffixes
        vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv']  # acceptable video suffixes
        images = [x for x in files if x.split('.')[-1].lower() in img_formats]
        videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]

        if self.dev:
            videos = [p]

        ni, nv = len(images), len(videos)

        self.files = images + videos
        self.screen = screen
        self.nf = ni + nv  # number of files
        self.video_flag = [False] * ni + [True] * nv
        self.mode = 'image'

#        if self.grab_screen:
#            self.nf = 1
#            self.video_flag = [False]
#            self.files = [p]

        if any(videos):
            self.new_video(videos[0])  # new video
        else:
            self.cap = None
        assert self.nf > 0, f'No images or videos found in {p}. ' \
                            f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}'

    def __iter__(self):
        self.count = 0
        return self

    def __next__(self):
        if self.count == self.nf:
            raise StopIteration

        path = self.files[self.count]

        if self.video_flag[self.count]:
            # Read video
            self.mode = 'video'
            ret_val, img0 = self.cap.read()
            if not ret_val:
                self.count += 1
                self.cap.release()
                if self.count == self.nf:  # last video
                    raise StopIteration
                else:
                    path = self.files[self.count]
                    self.new_video(path)
                    ret_val, img0 = self.cap.read()

#            if self.dev:
#                if (cv2.waitKey(1) & 0xFF) == ord('q'):
#                    raise StopIteration

            self.frame += 1
            print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='')

#        elif self.grab_screen:
#            self.mode = 'video'
#            img0 = grab(self.screen)
#            assert img0 is not None, 'Frame Error'
#            if (cv2.waitKey(1) & 0xFF) == ord('q'):
#                raise StopIteration
        else:
            # Read image
            self.count += 1
            img0 = cv2.imread(path)  # BGR
            assert img0 is not None, 'Image Not Found ' + path
            #print(f'image {self.count}/{self.nf} {path}: ', end='')

        # Padded resize
        img0 = cv2.resize(img0, (1280,720), interpolation=cv2.INTER_LINEAR)

        return path, img0, self.cap

    def new_video(self, path):
        self.frame = 0
        self.cap = cv2.VideoCapture(path)
        self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
        if self.dev:
            self.nframes = 250

    def __len__(self):
        return self.nf  # number of files

def increment_path(path, exist_ok=True, sep=''):
    # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc.
    path = Path(path)  # os-agnostic
    if (path.exists() and exist_ok) or (not path.exists()):
        return str(path)
    else:
        dirs = glob.glob(f"{path}{sep}*")  # similar paths
        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
        i = [int(m.groups()[0]) for m in matches if m]  # indices
        n = max(i) + 1 if i else 2  # increment number
        return f"{path}{sep}{n}"  # update path

def letterbox(
    img,
    new_shape=(320, 320),
    color=(114, 114, 114),
    auto=True,
    scaleFill=False,
    scaleup=True,
    stride=32,
):
    # Resize and pad image while meeting stride-multiple constraints
    shape = img.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)

    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])

    if not scaleup:  # only scale down, do not scale up (for better test mAP)
        r = min(r, 1.0)

    # Compute padding
    ratio = r, r  # width, height ratios
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[
        1]  # wh padding
    if auto:  # minimum rectangle
        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
    elif scaleFill:  # stretch
        dw, dh = 0.0, 0.0
        new_unpad = (new_shape[1], new_shape[0])
        ratio = new_shape[1] / shape[1], new_shape[0] / shape[
            0]  # width, height ratios

    # divide padding into 2 sides
    dw /= 2
    dh /= 2

    if shape[::-1] != new_unpad:  # resize
        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)

    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))


    img = cv2.copyMakeBorder(
        img,
        top,
        bottom,
        left,
        right,
        cv2.BORDER_CONSTANT,
        value=color,
    )  # add border

    return img, ratio, (dw, dh)


def _make_grid(nx=20, ny=20):
    xv, yv = np.meshgrid(np.arange(0, nx), np.arange(0, ny))
    return np.stack((xv, yv), 2).reshape((1, 1, ny, nx, 2)).astype('float32')


def _sigmoid(arr):
    arr = np.array(arr, dtype=np.float32)
    return 1.0 / (1.0 + np.exp(-1.0 * arr))


def split_for_trace_model(pred=None, anchor_grid=None):
    z = []
    st = [8, 16, 32]

    for i in range(3):
        bs, _, ny, nx = pred[i].shape
        pred[i] = pred[i].reshape(bs, 3, 85, ny, nx).transpose(0, 1, 3, 4, 2)
        y = _sigmoid(pred[i])
        gr = _make_grid(nx, ny)
        y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + gr) * st[i]  # xy
        y[..., 2:4] = (y[..., 2:4] * 2)**2 * anchor_grid[i]  # wh
        z.append(y.reshape(bs, -1, 85))

    pred = np.concatenate(z, 1)

    return pred


def _xywh2xyxy(x):
    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2]
    # where xy1=top-left, xy2=bottom-right
    y = np.copy(x)

    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y

    return y


def _box_iou(box1, box2):
    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
    """
    Return intersection-over-union (Jaccard index) of boxes.
    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
    Arguments:
        box1 (Tensor[N, 4])
        box2 (Tensor[M, 4])
    Returns:
        iou (Tensor[N, M]): the NxM matrix containing the pairwise
            IoU values for every element in boxes1 and boxes2
    """
    def box_area(box):
        # box = 4xn
        return (box[2] - box[0]) * (box[3] - box[1])

    area1 = box_area(box1.T)
    area2 = box_area(box2.T)

    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
    inter = (np.minimum(box1[:, None, 2:], box2[:, 2:]) -
             np.maximum(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
    return inter / (area1[:, None] + area2 - inter
                    )  # iou = inter / (area1 + area2 - inter)


def _nms(boxes, scores, iou_threshold):
    x1, y1 = boxes[:, 0], boxes[:, 1]
    x2, y2 = boxes[:, 2], boxes[:, 3]

    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    order = scores.argsort()[::-1]

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)

        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])

        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        ovr = inter / (areas[i] + areas[order[1:]] - inter)

        inds = np.where(ovr <= iou_threshold)[0]
        order = order[inds + 1]

    result = np.stack(keep)
    return result


def non_max_suppression(
        prediction,
        conf_thres=0.25,
        iou_thres=0.45,
        multi_label=False,
        labels=(),
):
    """Runs Non-Maximum Suppression (NMS) on inference results

    Returns:
         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
    """

    nc = prediction.shape[2] - 5  # number of classes
    xc = prediction[..., 4] > conf_thres  # candidates

    # Settings
    max_det = 300  # maximum number of detections per image
    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
    time_limit = 10.0  # seconds to quit after
    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)

    t = time.time()
    output = [np.zeros((0, 6))] * prediction.shape[0]
    for xi, x in enumerate(prediction):  # image index, image inference
        # Apply constraints
        x = x[xc[xi]]  # confidence

        # Cat apriori labels if autolabelling
        if labels and len(labels[xi]):
            l = labels[xi]
            v = np.zeros((len(l), nc + 5), device=x.device)
            v[:, :4] = l[:, 1:5]  # box
            v[:, 4] = 1.0  # conf
            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
            x = np.concatenate((x, v), 0)

        # If none remain process next image
        if not x.shape[0]:
            continue

        # Compute conf
        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf

        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
        box = _xywh2xyxy(x[:, :4])

        # Detections matrix nx6 (xyxy, conf, cls)
        if multi_label:
            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
            x = np.concatenate((box[i], x[i, j + 5, None], j[:, None].float()),
                               1)
        else:  # best class only
            conf = np.max(x[:, 5:], axis=1, keepdims=True)
            j = np.argmax(x[:, 5:], axis=1)
            j = j.reshape((j.shape[0], 1))
            x = np.concatenate((box, conf, j.astype('float32')),
                               1)[conf.reshape(-1) > conf_thres]

        # Check shape
        n = x.shape[0]  # number of boxes
        if not n:  # no boxes
            continue
        elif n > max_nms:  # excess boxes
            x = x[x[:, 4].argsort(
                descending=True)[:max_nms]]  # sort by confidence

        # NMS
        boxes, scores = x[:, :4], x[:, 4]  # boxes (offset by class), scores
        i = _nms(
            boxes,
            scores,
            iou_thres,
        )

        if i.shape[0] > max_det:  # limit detections
            i = i[:max_det]

        output[xi] = x[i]
        if (time.time() - t) > time_limit:
            print(f'WARNING: NMS time limit {time_limit}s exceeded')
            break  # time limit exceeded

    return output


def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
    # Rescale coords (xyxy) from img1_shape to img0_shape
    if ratio_pad is None:  # calculate from img0_shape
        gain = min(img1_shape[0] / img0_shape[0],
                   img1_shape[1] / img0_shape[1])  # gain  = old / new
        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (
            img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
    else:
        gain = ratio_pad[0][0]
        pad = ratio_pad[1]

    coords[:, [0, 2]] -= pad[0]  # x padding
    coords[:, [1, 3]] -= pad[1]  # y padding
    coords[:, :4] /= gain
    _clip_coords(coords, img0_shape)
    return coords


def _clip_coords(boxes, img_shape):
    # Clip bounding xyxy bounding boxes to image shape (height, width)
    boxes[:, 0] = np.clip(boxes[:, 0], 0, img_shape[1])  # x1
    boxes[:, 1] = np.clip(boxes[:, 1], 0, img_shape[0])  # y1
    boxes[:, 2] = np.clip(boxes[:, 2], 0, img_shape[1])  # x2
    boxes[:, 3] = np.clip(boxes[:, 3], 0, img_shape[0])  # y1


def driving_area_mask(seg, pad_wh=None):
    if pad_wh is None:
        return 1.0 - seg[0][0]
    else:
        temp_seg = copy.deepcopy(seg[0][0])

        pad_w = int(pad_wh[0])
        pad_h = int(pad_wh[1])
        seg_width = int(temp_seg.shape[1])
        seg_height = int(temp_seg.shape[0])

        temp_seg = temp_seg[pad_h:seg_height - pad_h, pad_w:seg_width - pad_w]

        return 1.0 - temp_seg


def lane_line_mask(ll, pad_wh=None):
    if pad_wh is None:
        return ll[0][0]
    else:
        temp_ll = copy.deepcopy(ll[0][0])

        pad_w = int(pad_wh[0])
        pad_h = int(pad_wh[1])
        seg_width = int(temp_ll.shape[1])
        seg_height = int(temp_ll.shape[0])

        temp_ll = temp_ll[pad_h:seg_height - pad_h, pad_w:seg_width - pad_w]

        return temp_ll

Since the original question I have tried to make the static input dimensions smaller (320 instead of 640) mainly for speed improvements.
6646.yolopv2.zip
yolopv2-artifacts.zip
Any help as to why the model is outputting the pattern would be appreciated.
If any further information is needed, let me know.
Thanks

4 months ago

0 Christina Kuruvilla 4 months ago

TI__Expert 4820 points

Hi Charanjit,

Some questions for clarification.Utils_onnx.py is what you use for inference right? Did you try to run under TIDL tools OSRT and receive the same issues?

I believe my comment from the related E2e may have been misunderstood. I apologies for the unclear wording on my part. TIDL does not support dynamic input, as you had observed, since the C7x uses fixed input dimensions. I was trying to suggest that the issue could be that the modification was done improperly to change it to fixed. However, if you have checked this, it is probably not the issue.

Also, could you share the model configurations for your model, as well the compilation log before you made this change?

Also are you running this in emulation or directly onto the SOC? If you are testing on the device, I recommend first to run it on PC emulation and ensure it is working there, before running on the device. Also, just to make sure, when you run the model without C7x offload / fully on ARM, you are using the -d option, correct?

Again, apologies for my unclear wording that resulted in some confusion.

Warm regards,

Christina

0 Charanjit Singh 4 months ago in reply to Christina Kuruvilla

Prodigy 110 points

Hi Christina,

Thank you for your reply.

utils_onnx.py just contains some functions which are used to get and process the input and results of the model. The actual inference takes place in sample.py from line 222 to 276 for various cases (TIDL compile, no offload, TIDLExecutionProvider).

What exactly is meant by "run under TIDL tools OSRT"? From what I understood TI recommends using OSRT (onnxrt, tflite etc.) If TIDL tools OSRT is something different then I have not tried it. I have only tried using onnxrt.

Christina Kuruvilla said:
Also, could you share the model configurations for your model, as well the compilation log before you made this change?

Again I am not sure what model configurations and compilation logs in this context means. I used the model available at the yolopv2 github release page, and used a jupyter notebook for converting the model to onnx. Following modifications were done in order to make the model use fixed shape instead of the dynamic ones:

import torch

device = 'cpu'
model_path = 'weight/yolopv2.pt'

model = torch.jit.load(model_path)
model = model.to(device)

def convert_to_onnx(net, file_name='output.onnx', input_shape=(320, 192), device='cpu', verbose=False):
    input = torch.randn(1, 3, input_shape[1], input_shape[0]).to(device)

    torch.onnx.export(
        net,
        input,
        file_name,
        input_names=['input_image'],
        # dynamic_axes={
        #     'input_image': {2: 'height', 3:'width'}
        # },
        verbose=verbose,
        #dynamo=True
    )
    
convert_to_onnx(model, 'YOLOPv2.onnx')

- torch.jit.load was used instead of torch.load

- input shape was changed from (640,640) to (320,192). Also used (640,384) once with the same result. The size has been calculated from the output of utils_onnx letterbox function which is used for the inference.

Christina Kuruvilla said:
Also are you running this in emulation or directly onto the SOC?

I was using the SOC directly for inference with offload and without offload, but have now tried emulation mode too in a Docker container. When running in emulation mode I am not sure if TIDLExecutionProvider is being used as the inference runs really slow (like on the A72 or normal PC inference without offload to GPU). The output of verbose logging however shows that the TIDLExecutionProvider is being used.

verbose logging in Docker on PC:

root@2730038e794a:/home/root/yolopv2# python3 sample.py --source calib-imgs/ --name pcinfer --save --log 0
compile_options: {'artifacts_folder': 'custom-artifacts/yolopv2', 'tidl_tools_path': '/home/root/tools/AM68PA/tidl_tools', 'advanced_options:c7x_firmware_version': '10_01_04_00'}
2025-05-11 20:18:53.196433964 [I:onnxruntime:, inference_session.cc:284 operator()] Flush-to-zero and denormal-as-zero are off
2025-05-11 20:18:53.196454243 [I:onnxruntime:, inference_session.cc:292 ConstructorCommon] Creating and using per session threadpools since use_per_session_threads_ is true
2025-05-11 20:18:53.196465584 [I:onnxruntime:, inference_session.cc:310 ConstructorCommon] Dynamic block base set to 0
libtidl_onnxrt_EP loaded 0x5ad872a1b900 
2025-05-11 20:18:53.318332624 [I:onnxruntime:, inference_session.cc:1344 Initialize] Initializing session.
2025-05-11 20:18:53.318370065 [I:onnxruntime:, session_state.cc:98 SetupAllocators] Allocator already registered for OrtMemoryInfo:[name:Cpu id:0 OrtMemType:0 OrtAllocatorType:1 Device:[DeviceType:0 MemoryType:0 DeviceId:0]]. Ignoring allocator from CPUExecutionProvider
2025-05-11 20:18:53.321037530 [I:onnxruntime:, constant_sharing.cc:257 ApplyImpl] Total shared scalar initializer count: 5
2025-05-11 20:18:53.327281541 [I:onnxruntime:, reshape_fusion.cc:53 ApplyImpl] Total fused reshape node count: 0
2025-05-11 20:18:53.328490271 [I:onnxruntime:, constant_sharing.cc:257 ApplyImpl] Total shared scalar initializer count: 0
2025-05-11 20:18:53.331814015 [I:onnxruntime:, reshape_fusion.cc:53 ApplyImpl] Total fused reshape node count: 0
2025-05-11 20:18:53.332639486 [I:onnxruntime:, constant_sharing.cc:257 ApplyImpl] Total shared scalar initializer count: 0
2025-05-11 20:18:53.335686764 [I:onnxruntime:, reshape_fusion.cc:53 ApplyImpl] Total fused reshape node count: 0
2025-05-11 20:18:53.336480835 [I:onnxruntime:, constant_sharing.cc:257 ApplyImpl] Total shared scalar initializer count: 0
2025-05-11 20:18:53.339562238 [I:onnxruntime:, reshape_fusion.cc:53 ApplyImpl] Total fused reshape node count: 0
2025-05-11 20:18:53.340366969 [I:onnxruntime:, constant_sharing.cc:257 ApplyImpl] Total shared scalar initializer count: 0
2025-05-11 20:18:53.343480824 [I:onnxruntime:, reshape_fusion.cc:53 ApplyImpl] Total fused reshape node count: 0
2025-05-11 20:18:53.344289592 [I:onnxruntime:, constant_sharing.cc:257 ApplyImpl] Total shared scalar initializer count: 0
2025-05-11 20:18:53.347972530 [I:onnxruntime:, reshape_fusion.cc:53 ApplyImpl] Total fused reshape node count: 0
2025-05-11 20:18:53.348784605 [I:onnxruntime:, constant_sharing.cc:257 ApplyImpl] Total shared scalar initializer count: 0
2025-05-11 20:18:53.351818147 [I:onnxruntime:, reshape_fusion.cc:53 ApplyImpl] Total fused reshape node count: 0
2025-05-11 20:18:53.352612579 [I:onnxruntime:, constant_sharing.cc:257 ApplyImpl] Total shared scalar initializer count: 0
2025-05-11 20:18:53.355656110 [I:onnxruntime:, reshape_fusion.cc:53 ApplyImpl] Total fused reshape node count: 0
2025-05-11 20:18:53.356449820 [I:onnxruntime:, constant_sharing.cc:257 ApplyImpl] Total shared scalar initializer count: 0
2025-05-11 20:18:53.359519591 [I:onnxruntime:, reshape_fusion.cc:53 ApplyImpl] Total fused reshape node count: 0
2025-05-11 20:18:53.360316808 [I:onnxruntime:, constant_sharing.cc:257 ApplyImpl] Total shared scalar initializer count: 0
2025-05-11 20:18:53.363396177 [I:onnxruntime:, reshape_fusion.cc:53 ApplyImpl] Total fused reshape node count: 0
Final number of subgraphs created are : 3, - Offloaded Nodes - 419, Total Nodes - 424 
2025-05-11 20:18:53.724917241 [W:onnxruntime:, graph.cc:3533 CleanUnusedInitializersAndNodeArgs] Removing initializer '777'. It is not used by any node and should be removed from the model.
2025-05-11 20:18:53.724951567 [W:onnxruntime:, graph.cc:3533 CleanUnusedInitializersAndNodeArgs] Removing initializer '776'. It is not used by any node and should be removed from the model.
2025-05-11 20:18:53.724959832 [W:onnxruntime:, graph.cc:3533 CleanUnusedInitializersAndNodeArgs] Removing initializer '775'. It is not used by any node and should be removed from the model.
2025-05-11 20:18:53.877549348 [V:onnxruntime:, session_state.cc:1149 VerifyEachNodeIsAssignedToAnEp] Node placements
2025-05-11 20:18:53.877576420 [V:onnxruntime:, session_state.cc:1155 VerifyEachNodeIsAssignedToAnEp]  Node(s) placed on [TIDLExecutionProvider]. Number of nodes: 3
2025-05-11 20:18:53.877581650 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   TIDL_0 (TIDLExecutionProvider_TIDL_0_0)
2025-05-11 20:18:53.877591298 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   TIDL_1 (TIDLExecutionProvider_TIDL_1_1)
2025-05-11 20:18:53.877598381 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   TIDL_2 (TIDLExecutionProvider_TIDL_2_2)
2025-05-11 20:18:53.877607519 [V:onnxruntime:, session_state.cc:1155 VerifyEachNodeIsAssignedToAnEp]  Node(s) placed on [CPUExecutionProvider]. Number of nodes: 9
2025-05-11 20:18:53.877614432 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   Expand (/Expand)
2025-05-11 20:18:53.877621155 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   SequenceConstruct (/SequenceConstruct)
2025-05-11 20:18:53.877628619 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   MaxPool (/MaxPool_5_output_0_nchwc)
2025-05-11 20:18:53.877635332 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   ReorderInput (ReorderInput)
2025-05-11 20:18:53.877642485 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   MaxPool (/MaxPool_4_output_0_nchwc)
2025-05-11 20:18:53.877650380 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   MaxPool (/MaxPool_3_output_0_nchwc)
2025-05-11 20:18:53.877658155 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   ReorderOutput (ReorderOutput)
2025-05-11 20:18:53.877665179 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   ReorderOutput (ReorderOutput_token_245)
2025-05-11 20:18:53.877671941 [V:onnxruntime:, session_state.cc:1157 VerifyEachNodeIsAssignedToAnEp]   ReorderOutput (ReorderOutput_token_246)
2025-05-11 20:18:53.877769467 [V:onnxruntime:, session_state.cc:133 CreateGraphInfo] SaveMLValueNameIndexMapping
2025-05-11 20:18:53.877840542 [V:onnxruntime:, session_state.cc:179 CreateGraphInfo] Done saving OrtValue mappings.
2025-05-11 20:18:53.877850912 [I:onnxruntime:, allocation_planner.cc:2376 CreateGraphPartitioner] Use DeviceBasedPartition as default
2025-05-11 20:18:53.878104224 [I:onnxruntime:, session_state_utils.cc:201 SaveInitializedTensors] Saving initialized tensors.
2025-05-11 20:18:53.966352636 [I:onnxruntime:, session_state_utils.cc:344 SaveInitializedTensors] Done saving initialized tensors
The soft limit is 10240
The hard limit is 10240
MEM: Init ... !!!
MEM: Init ... Done !!!
 0.0s:  VX_ZONE_INIT:Enabled
 0.25s:  VX_ZONE_ERROR:Enabled
 0.30s:  VX_ZONE_WARNING:Enabled
 0.3869s:  VX_ZONE_INIT:[tivxInit:190] Initialization Done !!!
2025-05-11 20:18:54.102611641 [I:onnxruntime:, inference_session.cc:1645 Initialize] Session successfully initialized.
EP: ['TIDLExecutionProvider', 'CPUExecutionProvider']
/home/root/yolopv2/calib-imgs
2025-05-11 20:18:54.118767160 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:18:54.118868973 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:18:58.736539542 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:18:58.736726848 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:18:58.736836547 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:18:58.736903735 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:18:58.736968688 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:18:58.737182475 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:18:58.737249773 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:18:58.737300058 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:18:58.737315999 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:19:00.893704254 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:19:00.893740553 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:19:00.893750763 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:19:00.893759900 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:19:00.893773165 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:19:00.893784036 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:19:00.893795047 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:19:00.893804665 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:19:00.893975220 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:19:00.893986992 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:19:02.011597300 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:19:02.011631836 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:19:02.011644209 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:19:02.011653066 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:19:02.011848778 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:19:02.011861793 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:19:02.011871742 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:19:02.011881030 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/0023.jpg
2025-05-11 20:19:02.080280880 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:19:02.080388665 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:19:06.678981950 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:19:06.679063425 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:19:06.679171540 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:19:06.679222727 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:19:06.679281239 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:19:06.679325483 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:19:06.679390196 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:19:06.679441253 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:19:06.679449038 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:19:08.807123054 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:19:08.807153813 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:19:08.807159664 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:19:08.807164844 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:19:08.807170164 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:19:08.807175334 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:19:08.807183429 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:19:08.807188669 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:19:08.807346379 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:19:08.807354134 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:19:09.904065447 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:19:09.904091897 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:19:09.904097468 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:19:09.904103008 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:19:09.904844769 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:19:09.904853645 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:19:09.904858445 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:19:09.904865748 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d0639bd2-fcc27704.jpg
2025-05-11 20:19:09.949726162 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:19:09.949818237 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:19:14.534101348 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:19:14.534169057 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:19:14.534262174 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:19:14.534309414 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:19:14.534371873 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:19:14.534417379 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:19:14.534490428 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:19:14.534536596 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:19:14.534542838 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:19:16.645421095 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:19:16.645450712 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:19:16.645456503 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:19:16.645461382 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:19:16.645466822 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:19:16.645471541 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:19:16.645477262 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:19:16.645482542 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:19:16.645611588 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:19:16.645616417 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:19:17.753193667 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:19:17.753221069 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:19:17.753227371 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:19:17.753232641 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:19:17.753409047 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:19:17.753419426 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:19:17.753425398 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:19:17.753430377 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d09b7eb3-bf9bfb1a.jpg
2025-05-11 20:19:17.796113908 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:19:17.796208938 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:19:22.382463228 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:19:22.382543962 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:19:22.382644904 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:19:22.382679219 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:19:22.382794418 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:19:22.382854532 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:19:22.382959702 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:19:22.383019045 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:19:22.383028903 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:19:24.472483733 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:19:24.472513569 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:19:24.472519010 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:19:24.472523669 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:19:24.472528287 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:19:24.472532716 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:19:24.472538126 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:19:24.472542915 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:19:24.472673253 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:19:24.472682981 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:19:25.578142763 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:19:25.578177679 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:19:25.578186827 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:19:25.578195243 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:19:25.578395433 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:19:25.578410171 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:19:25.578419299 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:19:25.578428185 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d178f7e5-e4bd6afb.jpg
2025-05-11 20:19:25.637767415 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:19:25.637859390 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:19:30.196375233 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:19:30.196442912 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:19:30.196513316 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:19:30.196539996 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:19:30.196702064 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:19:30.196755275 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:19:30.196832823 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:19:30.196881225 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:19:30.196887416 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:19:32.343557150 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:19:32.343592026 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:19:32.343600552 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:19:32.343611934 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:19:32.343621893 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:19:32.343630910 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:19:32.343640578 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:19:32.343650828 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:19:32.343818887 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:19:32.343831401 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:19:33.435163382 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:19:33.435189702 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:19:33.435195874 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:19:33.435200823 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:19:33.435360957 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:19:33.435368902 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:19:33.435374784 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:19:33.435380755 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d1e828e1-2beebb9b.jpg
2025-05-11 20:19:33.477111619 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:19:33.477199095 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:19:38.053259583 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:19:38.053339104 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:19:38.053443262 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:19:38.053503256 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:19:38.053565745 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:19:38.053613656 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:19:38.053682497 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:19:38.053741589 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:19:38.053751708 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:19:40.181119714 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:19:40.181159620 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:19:40.181171412 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:19:40.181180710 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:19:40.181199295 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:19:40.181207190 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:19:40.181216668 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:19:40.181227729 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:19:40.181443108 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:19:40.181455361 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:19:41.293619118 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:19:41.293654265 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:19:41.293664124 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:19:41.293673271 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:19:41.293856149 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:19:41.293867220 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:19:41.293876487 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:19:41.293886005 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d2154c1d-ab1ef472.jpg
2025-05-11 20:19:41.353155871 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:19:41.353247345 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:19:45.976210480 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:19:45.976274682 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:19:45.976376946 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:19:45.976432552 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:19:45.976489380 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:19:45.976534295 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:19:45.976598036 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:19:45.976648723 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:19:45.976654724 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:19:48.108324079 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:19:48.108356962 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:19:48.108365558 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:19:48.108373934 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:19:48.108382711 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:19:48.108390776 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:19:48.108399132 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:19:48.108407237 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:19:48.108557583 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:19:48.108568644 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:19:49.201323574 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:19:49.201350586 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:19:49.201357088 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:19:49.201362598 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:19:49.201534264 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:19:49.201542550 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:19:49.201547700 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:19:49.201554653 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d2216bdb-389095e2.jpg
2025-05-11 20:19:49.244725179 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:19:49.244813396 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:19:53.865100013 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:19:53.865181137 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:19:53.865293280 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:19:53.865360718 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:19:53.865422996 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:19:53.865471929 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:19:53.865543074 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:19:53.865600172 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:19:53.865610292 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:19:55.990989366 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:19:55.991022028 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:19:55.991031035 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:19:55.991039270 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:19:55.991047526 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:19:55.991055652 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:19:55.991065120 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:19:55.991073275 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:19:55.991224282 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:19:55.991232848 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:19:57.093081511 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:19:57.093114974 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:19:57.093126366 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:19:57.093136395 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:19:57.093325684 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:19:57.093337587 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:19:57.093346754 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:19:57.093355831 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d350d794-6be69a57.jpg
2025-05-11 20:19:57.151197157 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:19:57.151290705 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:20:01.679765847 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:20:01.679835319 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:20:01.679974864 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:20:01.680032002 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:20:01.680088950 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:20:01.680137052 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:20:01.680199851 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:20:01.680250327 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:20:01.680256909 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:20:03.809795605 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:20:03.809831654 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:20:03.809840330 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:20:03.809848586 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:20:03.809858805 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:20:03.809867582 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:20:03.809877030 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:20:03.809886588 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:20:03.810048616 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:20:03.810059967 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:20:04.900458776 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:20:04.900494374 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:20:04.900504233 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:20:04.900513180 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:20:04.900699794 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:20:04.900711476 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:20:04.900721275 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:20:04.900730232 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d4782392-9aee10b3.jpg
2025-05-11 20:20:04.959178486 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:20:04.959265992 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:20:09.596019979 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:20:09.596099089 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:20:09.596207846 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:20:09.596258762 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:20:09.596337301 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:20:09.596392045 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:20:09.596463761 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:20:09.596518165 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:20:09.596528254 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:20:11.746632659 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:20:11.746668987 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:20:11.746678015 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:20:11.746686300 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:20:11.746694576 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:20:11.746713662 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:20:11.746726406 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:20:11.746738700 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:20:11.746893363 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:20:11.746917820 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:20:12.846096907 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:20:12.846132484 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:20:12.846148024 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:20:12.846161269 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:20:12.846354716 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:20:12.846368542 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:20:12.846379603 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:20:12.846391967 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d5027b27-c5cb186a.jpg
2025-05-11 20:20:12.905005328 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:20:12.905092643 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:20:17.514207499 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:20:17.514287271 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:20:17.514398251 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:20:17.514447705 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:20:17.514505815 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:20:17.514548727 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:20:17.514614862 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:20:17.514666701 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:20:17.514674045 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:20:19.661025212 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:20:19.661053697 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:20:19.661059047 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:20:19.661063996 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:20:19.661069426 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:20:19.661074326 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:20:19.661080016 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:20:19.661087671 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:20:19.661221475 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:20:19.661228769 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:20:20.761300138 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:20:20.761337028 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:20:20.761350423 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:20:20.761365452 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:20:20.761555572 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:20:20.761568467 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:20:20.761579728 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:20:20.761591150 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d58ae42c-3e2f6675.jpg
2025-05-11 20:20:20.819630316 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:20:20.819726067 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:20:25.462761315 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:20:25.462847218 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:20:25.462983116 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:20:25.463129413 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:20:25.464094434 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:20:25.464164006 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:20:25.464238277 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:20:25.464297769 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:20:25.464308430 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:20:27.604992009 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:20:27.605021374 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:20:27.605028247 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:20:27.605034479 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:20:27.605041092 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:20:27.605048907 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:20:27.605055950 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:20:27.605064015 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:20:27.605229970 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:20:27.605238256 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:20:28.711022542 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:20:28.711056627 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:20:28.711066646 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:20:28.711075713 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:20:28.711269120 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:20:28.711280431 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:20:28.711290110 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:20:28.711301682 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d59b65a4-3b5b5ea4.jpg
2025-05-11 20:20:28.772948044 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:20:28.773040229 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:20:33.416597254 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:20:33.416776123 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:20:33.416843250 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:20:33.416892454 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:20:33.417056435 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:20:33.417101640 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:20:33.417165461 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:20:33.417215055 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:20:33.417222920 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:20:35.593508382 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:20:35.593535894 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:20:35.593542707 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:20:35.593548278 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:20:35.593553548 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:20:35.593558387 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:20:35.593564208 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:20:35.593569678 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:20:35.593703392 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:20:35.593709423 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:20:36.705561187 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:20:36.705594540 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:20:36.705605080 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:20:36.705615891 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:20:36.705804338 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:20:36.705815008 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:20:36.705824116 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:20:36.705833323 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d764e361-fcf8e72c.jpg
2025-05-11 20:20:36.767620006 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:20:36.767705659 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:20:41.359372797 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:20:41.359554631 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:20:41.359627309 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:20:41.359684357 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:20:41.359744962 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:20:41.359796480 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:20:41.359868466 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:20:41.359995718 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:20:41.360004945 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:20:43.509108337 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:20:43.509146379 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:20:43.509161087 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:20:43.509174603 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:20:43.509184331 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:20:43.509193799 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:20:43.509203958 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:20:43.509214047 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:20:43.509367879 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:20:43.509378319 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:20:44.630686367 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:20:44.630719570 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:20:44.630728878 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:20:44.630737504 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:20:44.630935129 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:20:44.630946711 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:20:44.630956750 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:20:44.630965847 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d795d9d8-2323e9d8.jpg
2025-05-11 20:20:44.691935426 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:20:44.692023343 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:20:49.281320431 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:20:49.281509880 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:20:49.281598228 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:20:49.281642862 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:20:49.281703898 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:20:49.281756979 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:20:49.281830137 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:20:49.281888177 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:20:49.281898798 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:20:51.437450196 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:20:51.437485653 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:20:51.437495772 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:20:51.437505861 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:20:51.437515910 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:20:51.437525749 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:20:51.437535748 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:20:51.437545567 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:20:51.437704979 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:20:51.437715098 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:20:52.531152926 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:20:52.531187120 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:20:52.531199364 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:20:52.531210415 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:20:52.531398571 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:20:52.531410604 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:20:52.531420623 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:20:52.531430261 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d83e92cf-45f97194.jpg
2025-05-11 20:20:52.590863104 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:20:52.590964085 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:20:57.159576366 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:20:57.159746248 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:20:57.159811912 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:20:57.159847870 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:20:57.159926540 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:20:57.159973889 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:20:57.160094528 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:20:57.160150925 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:20:57.160161535 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:20:59.297201082 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:20:59.297235227 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:20:59.297244034 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:20:59.297252429 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:20:59.297263881 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:20:59.297274642 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:20:59.297284651 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:20:59.297294219 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:20:59.297448812 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:20:59.297459211 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:21:00.411437548 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:21:00.411474939 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:21:00.411484998 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:21:00.411494717 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:21:00.411689095 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:21:00.411701078 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:21:00.411710365 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:21:00.411719913 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d86afd0b-8c6700fa.jpg
2025-05-11 20:21:00.475055139 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:21:00.475142845 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:21:05.115882536 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:21:05.116067036 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:21:05.116119275 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:21:05.116166875 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:21:05.116223382 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:21:05.116271223 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:21:05.116336156 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:21:05.116388485 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:21:05.116395729 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:21:07.261775525 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:21:07.261813196 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:21:07.261824467 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:21:07.261836620 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:21:07.261850817 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:21:07.261863802 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:21:07.261878420 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:21:07.261891394 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:21:07.262063550 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:21:07.262076735 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:21:08.362478051 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:21:08.362512296 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:21:08.362522605 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:21:08.362531823 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:21:08.362719448 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:21:08.362732613 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:21:08.362746920 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:21:08.362759143 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/d88373bc-b4b9aae3.jpg
2025-05-11 20:21:08.423562629 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:21:08.423659021 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:21:13.004713889 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:21:13.004890644 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:21:13.004967009 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:21:13.005017344 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:21:13.005081355 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:21:13.005122674 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:21:13.005189119 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:21:13.005242901 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:21:13.005250245 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:21:15.136183646 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:21:15.136217831 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:21:15.136229022 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:21:15.136238330 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:21:15.136247898 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:21:15.136257015 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:21:15.136266002 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:21:15.136274428 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:21:15.136432538 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:21:15.136443158 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:21:16.228118558 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:21:16.228152933 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:21:16.228165377 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:21:16.228175586 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:21:16.228370956 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:21:16.228383089 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:21:16.228392146 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:21:16.228401244 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/da5035aa-3c834838.jpg
2025-05-11 20:21:16.287882343 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:21:16.287986159 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:21:20.892517860 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:21:20.892605206 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:21:20.892708732 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:21:20.892749519 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:21:20.892807880 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:21:20.892846443 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:21:20.892931744 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:21:20.892982140 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:21:20.892990285 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:21:23.012565509 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:21:23.012601567 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:21:23.012610885 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:21:23.012619872 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:21:23.012631504 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:21:23.012640781 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:21:23.012650900 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:21:23.012660679 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:21:23.012817787 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:21:23.012828777 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:21:24.096175903 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:21:24.096209546 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:21:24.096219545 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:21:24.096228422 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:21:24.096416759 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:21:24.096428681 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:21:24.096437638 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:21:24.096447487 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/db145068-5049c9a4.jpg
2025-05-11 20:21:24.157404230 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:21:24.157493509 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:21:28.751770413 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:21:28.751970823 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:21:28.752026127 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:21:28.752065812 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:21:28.752121548 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:21:28.752179979 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:21:28.752246264 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:21:28.752297181 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:21:28.752304715 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:21:30.872239379 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:21:30.872273644 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:21:30.872283703 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:21:30.872292579 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:21:30.872301386 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:21:30.872310213 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:21:30.872319460 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:21:30.872329179 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:21:30.872481978 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:21:30.872491135 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:21:31.981978781 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:21:31.982014349 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:21:31.982025930 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:21:31.982036430 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:21:31.982226400 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:21:31.982238012 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:21:31.982247069 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:21:31.982256337 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/db24bc52-04a0d171.jpg
2025-05-11 20:21:32.045445287 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:21:32.045533905 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:21:36.671566570 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:21:36.671731452 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:21:36.671782840 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:21:36.671821202 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:21:36.671873171 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:21:36.671943103 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:21:36.672009950 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:21:36.672060916 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:21:36.672069342 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:21:38.842716392 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:21:38.842752380 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:21:38.842763151 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:21:38.842772579 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:21:38.842782658 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:21:38.842792015 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:21:38.842802115 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:21:38.842812113 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:21:38.842982757 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:21:38.842994248 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:21:39.944391147 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:21:39.944426714 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:21:39.944437675 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:21:39.944446061 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:21:39.944645568 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:21:39.944657070 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:21:39.944666328 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:21:39.944675345 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/db55c4fe-b3b194fc.jpg
2025-05-11 20:21:40.003437385 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:21:40.003530091 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:21:44.723024293 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:21:44.723091580 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:21:44.723188123 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:21:44.723237697 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:21:44.723300766 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:21:44.723351983 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:21:44.723433036 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:21:44.723484283 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:21:44.723490185 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:21:46.876441685 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:21:46.876471992 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:21:46.876478254 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:21:46.876483544 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:21:46.876488684 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:21:46.876493543 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:21:46.876499114 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:21:46.876506397 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:21:46.876642986 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:21:46.876650721 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:21:47.967751811 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:21:47.967781216 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:21:47.967787228 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:21:47.967792728 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:21:47.967982968 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:21:47.967990693 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:21:47.967995432 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:21:47.968001233 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/db6296e5-c12fbfc4.jpg
2025-05-11 20:21:48.026400146 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:21:48.026490848 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:21:52.633402384 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:21:52.633490731 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:21:52.633600830 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:21:52.633663158 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:21:52.633727369 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:21:52.633778206 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:21:52.633847266 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:21:52.633925034 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:21:52.633935553 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:21:54.762063594 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:21:54.762100033 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:21:54.762108579 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:21:54.762116625 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:21:54.762126123 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:21:54.762136582 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:21:54.762146581 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:21:54.762155658 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:21:54.762314068 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:21:54.762325089 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:21:55.867549865 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:21:55.867585352 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:21:55.867597345 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:21:55.867606873 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:21:55.867815277 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:21:55.867827260 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:21:55.867836347 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:21:55.867845134 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/db6d38a1-7a5956b4.jpg
2025-05-11 20:21:55.916143172 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:21:55.916231278 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:22:00.501172968 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:22:00.501321980 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:22:00.501372085 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:22:00.501410758 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:22:00.501468087 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:22:00.501514905 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:22:00.501582653 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:22:00.501632778 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:22:00.501639922 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:22:02.660092491 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:22:02.660126515 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:22:02.660135663 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:22:02.660143978 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:22:02.660152334 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:22:02.660162584 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:22:02.660172532 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:22:02.660182381 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:22:02.660351661 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:22:02.660364836 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:22:03.768200471 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:22:03.768230507 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:22:03.768238903 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:22:03.768246177 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:22:03.768455674 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:22:03.768471343 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:22:03.768482655 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:22:03.768489648 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/db6f31d4-d49858f5.jpg
2025-05-11 20:22:03.816266866 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:22:03.816357327 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:22:08.355924023 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:22:08.355992483 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:22:08.356056514 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:22:08.356083445 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:22:08.356197771 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:22:08.356238278 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:22:08.356312559 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:22:08.356357874 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:22:08.356365639 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:22:10.551400850 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:22:10.551437169 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:22:10.551446226 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:22:10.551454401 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:22:10.551462787 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:22:10.551472987 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:22:10.551485911 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:22:10.551495139 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:22:10.551654139 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:22:10.551664389 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:22:11.663013117 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:22:11.663042012 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:22:11.663048244 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:22:11.663053213 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:22:11.663234556 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:22:11.663245778 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:22:11.663254564 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:22:11.663263551 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/db79fe59-510c7b3b.jpg
2025-05-11 20:22:11.708194390 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:22:11.708282396 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:22:16.398864303 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:22:16.398943303 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:22:16.399005090 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:22:16.399028123 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:22:16.399275451 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:22:16.399320096 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:22:16.399398965 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:22:16.399441856 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:22:16.399448969 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:22:18.563021328 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:22:18.563052026 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:22:18.563061064 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:22:18.563068768 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:22:18.563077645 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:22:18.563086071 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:22:18.563094918 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:22:18.563103975 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:22:18.563279046 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:22:18.563289836 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:22:19.677378159 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:22:19.677405792 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:22:19.677411823 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:22:19.677417243 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:22:19.677616470 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:22:19.677625287 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:22:19.677633743 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:22:19.677641007 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/dbc2bc70-2b21c210.jpg
2025-05-11 20:22:19.723595940 [V:onnxruntime:, sequential_executor.cc:534 ExecuteThePlan] Number of streams: 1
2025-05-11 20:22:19.723686181 [V:onnxruntime:, sequential_executor.cc:184 SessionScope] Begin execution
2025-05-11 20:22:24.409370204 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 425
2025-05-11 20:22:24.409437922 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 429
2025-05-11 20:22:24.409498025 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 431
2025-05-11 20:22:24.409527040 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 434
2025-05-11 20:22:24.409680571 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 430
2025-05-11 20:22:24.409726788 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 433
2025-05-11 20:22:24.409797572 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 428
2025-05-11 20:22:24.409852877 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 309 released
2025-05-11 20:22:24.409860571 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 432
2025-05-11 20:22:26.562249138 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 102 released
2025-05-11 20:22:26.562278544 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 103 released
2025-05-11 20:22:26.562284104 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 104 released
2025-05-11 20:22:26.562290897 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 105 released
2025-05-11 20:22:26.562296748 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 106 released
2025-05-11 20:22:26.562302258 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 107 released
2025-05-11 20:22:26.562326394 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 306 released
2025-05-11 20:22:26.562332225 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 426
2025-05-11 20:22:26.562486958 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 1 released
2025-05-11 20:22:26.562496285 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 364
2025-05-11 20:22:27.662493804 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 3 released
2025-05-11 20:22:27.662521296 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 210 released
2025-05-11 20:22:27.662527107 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 211 released
2025-05-11 20:22:27.662532237 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 427
2025-05-11 20:22:27.662709672 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 4 released
2025-05-11 20:22:27.662721234 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 5 released
2025-05-11 20:22:27.662730131 [V:onnxruntime:, stream_execution_context.cc:171 RecycleNodeInputs] ort value 6 released
2025-05-11 20:22:27.662739248 [V:onnxruntime:, sequential_executor.cc:518 ExecuteKernel] stream 0 launch kernel with idx 424
 The image with the result is saved in: runs/detect/pcinfer4/dbe58c0d-8e9aef50.jpg
MEM: Deinit ... !!!
MEM: Alloc's: 95 alloc's of 199132879 bytes 
MEM: Free's : 95 free's  of 199132879 bytes 
MEM: Open's : 0 allocs  of 0 bytes 
MEM: Deinit ... Done !!!

Christina Kuruvilla said:
when you run the model without C7x offload / fully on ARM, you are using the -d option, correct?

Correct I am using -d option which uses only CPUExecutionProvider which I suppose is the A72 on the TDA4VM.

Let me know if you have any suggestions or questions for reproducing these steps on your side.

Regards,

Charanjit

0 Chris Tsongas 4 months ago in reply to Charanjit Singh

TI__Genius 14420 points

Hi Charanjit,

Trying to run your sample applications. Where does da_seg_mid come from? And any other modules needed.

Regards,

Chris

0 Charanjit Singh 4 months ago in reply to Chris Tsongas

Prodigy 110 points

Hi Chris,

da_seg_mid and steering are not relevant for the code above. Forgot to delete that part.

Let me know if anything else is needed from me for running the model.

Regards,

Charanjit

0 Charanjit Singh 3 months ago in reply to Chris Tsongas

Prodigy 110 points

Hi Chris,

Going through all the files I have uploaded, I realized that the yolopv2-artifacts.zip directory uploaded by me was empty. So I am uploading it here

0513.yolopv2-artifacts.zip

Regards,

Charanjit

Processors

Processors forum

SK-TDA4VM: YOLOPv2 Lane Line Detection distortion when off-loading to C7x